{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d6eb7cb",
   "metadata": {},
   "source": [
    "## 电商的商业模式"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bd0916",
   "metadata": {},
   "source": [
    "### 商业模式（分类）\n",
    "**要弄清楚公司的业务是B端业务还是C端业务，不同业务的需求和评价指标都是不同的**\n",
    "+ B2B: 商家对商家（企业卖家对企业卖家），交易双方都是企业，最典型的案例就是阿里巴巴，汇聚了各行业的供应商，特点是订单量一般较大\n",
    "+ B2C: 商家对个人（企业卖家对个人买家），例如：唯品会，聚美优品。\n",
    "+ B2B2C: 商家对商家个人，例如：天猫，京东。\n",
    "+ C2C: 个人卖家对个人卖家，例如：淘宝、人人车\n",
    "+ O2O: 线上售卖到线下提货，将线下的商务机会与互联网结合，让互联网成为线下交易平台，例如：美团、饿了么。\n",
    "+ C2B: 个人对商家（个人买家对企业卖家）， 先有消费者提出需求，后有商家按需求组织生产，例如：商品宅配。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0dd65e2",
   "metadata": {},
   "source": [
    "### 生命周期\n",
    "互联网和电商刚刚兴起的时候，只要吸引用户打开网页进入店面，就会有一定比例的人下单，持续产生转化。在电商的“流量红利时代”，用“$ 店铺访客 \\times 购买转化率 $”就可以估算出买家数，再用“$ 买家数 \\times 客单价 $”就可以估算成交额。\n",
    "\n",
    "随着电商的普及，拉新越来越困难（流量下滑、市场下沉、会员绑定），获客成本越来越高，用户也变得更加挑剔。电商行业不得不从“流量运营”转变为“精细化用户运营”，更加注重促成单个用户的多次购买以及老用户带动新用户的社交裂变。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16a704d",
   "metadata": {},
   "source": [
    "### 核心指标\n",
    "指标，具备业务含义，能够反映业务特征的数据，有几个要点：\n",
    "\n",
    "1. 指标必须是数值，不能是文本、日期等。\n",
    "2. 指标都是汇总计算出来的，单个明细数据不是指标。\n",
    "\n",
    "- 新老用户\n",
    "    - 新（老）用户数量占比\n",
    "    - 新（老）用户金额占比\n",
    "    \n",
    "- 复购率和回购率（**由于获客成本变高，实现1个用户的多次购买是十分重要的**）\n",
    "    - 复购率：复购（某段时间有2次及以上购买行为）用户的占比。复购率能反映用户的忠诚度，监测周期一般较长。\n",
    "    - 回购率：回购率一般监测周期较短，可以反映如短期促销活动对用户的吸引力。\n",
    "    \n",
    "- 用户交易常用指标\n",
    "    - 访问次数（PV page view）：一定时间内某个页面的浏览次数。\n",
    "    - 访问人数（UV unique vistor）：一定时间内访问某个页面的人数。\n",
    "    - 加购数：将某款商品加入到购物车的用户数。\n",
    "    - 收藏数：收藏某款商品的用户数。\n",
    "    - GMV（总交易额、成交总额）：Gross Merchandise Volume，通常说的交易“流水”。\n",
    "    - 客单价（ARPU Average Revenue Per User）：“$ 总收入 / 总用户数 $” ---> ARPPU(Average Revenue Per Paying Use)。\n",
    "    - 转化率：“$ 付费用户数 / 访客数 $”。\n",
    "    - 折扣率：“$ GMV / 吊牌总额 $”，其中吊牌总额为：“$ 吊牌价 \\times 销量 $”。\n",
    "    - 拒退量：拒收和退货的总数量。\n",
    "    - 拒退额：拒收和退货的总金额。\n",
    "    - 实际销售额：“$ GMV - 拒退额 $”。\n",
    "    \n",
    "- 商品管理常用指标\n",
    "    - SPU数：Standard Product Unit。\n",
    "    - SKU数：Standard Keeping Unit。\n",
    "    - 售卖比：“$ GMV / 备货值 $”，了解商品流转情况，可以用于库存优化。\n",
    "    - 动销率：“$ 有销量的SKU数 / 在售SKU数 $”。\n",
    "    <img src=\"eshop-db.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb1a35d",
   "metadata": {},
   "source": [
    "### 指标体系"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0892b203",
   "metadata": {},
   "source": [
    "一个指标没有办法解决复杂的业务问题，需要使用多个指标从不同维度来评估业务，所以指标体系是根据运营的目标，将多个指标创建联系后形成的整体。\n",
    "\n",
    "<img src=\"indicator-system.png\" width=\"600\">\n",
    "\n",
    "指标体系的作用：\n",
    "\n",
    "1. 监控业务状况。\n",
    "2. 通过拆解指标找到问题。\n",
    "3. 评估业务值得改进的地方。\n",
    "\n",
    "建立指标体系的步骤：\n",
    "\n",
    "1. 厘清业务阶段和方向。\n",
    "    - 创业期：关注盘子大小，用户体量，指标围绕用户量提升做各种维度的拆解。\n",
    "    - 发展期：关注产品健康度，优化当前用户量结构，提升留存率。\n",
    "    - 成熟期：关注市场份额，关注和收入相关的指标，做好市场份额和竞品的监控。\n",
    "2. 确定核心指标。(KPI 和 OKR都是业绩考核指标)\n",
    "    - 通过KPI/OKR，找到一级指标（北极星指标及其伴随指标）。\n",
    "   <img src=\"polaris-indicator.png\" width=\"600\">\n",
    "    - 了解业务运营状况，找到二级指标。\n",
    "    - 梳理业务流程，找到三级指标。\n",
    "3. 指标维度拆解。\n",
    "    <img src=\"disassemble.png\" width=\"600\">\n",
    "4. 指标的宣贯和落地。\n",
    "    - 业务埋点\n",
    "    - 创建报表\n",
    "    - 统一口径\n",
    "    - 更新周期\n",
    "\n",
    "指标体系搭建常见问题：\n",
    "\n",
    "1. 没有北极星指标，抓不住重点。\n",
    "2. 指标间没有逻辑联系，找不出问题。\n",
    "3. 指标缺乏业务意义，为了拆解而拆解。\n",
    "4. 缺乏和业务方的沟通。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21949c98",
   "metadata": {},
   "source": [
    "### 分析方法\n",
    "\n",
    "1. 对比分析法\n",
    "2. 相关分析法\n",
    "3. RFM模型分析法\n",
    "4. AARRR模型分析法\n",
    "5. 漏斗分析法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e87efd1",
   "metadata": {},
   "source": [
    "### 代码实操\n",
    "\n",
    "####  数据清洗\n",
    "1. 提取2019年的订单数据\n",
    "2. 处理业务流程不符的数据（支付时间早于下单时间、支付时长超过30分钟、订单金额小于0、支付金额小于0）\n",
    "3. 处理渠道为空的数据（补充众数）\n",
    "4. 处理平台类型字段（去掉多余空格，保持数据一致）\n",
    "5. 添加折扣字段，处理折扣大于1的字段（将支付金额改为‘订单金额’ * ‘平均折扣’）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "902e3a6e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f961bb02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "table.dataframe td, table.dataframe th {\n",
       "    border: 1px  blue solid !important;\n",
       "  color: black !important;\n",
       "} #设置df表格输出样式\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<style type=\"text/css\">\n",
    "table.dataframe td, table.dataframe th {\n",
    "    border: 1px  blue solid !important;\n",
    "  color: black !important;\n",
    "} #设置df表格输出样式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "09f9b429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>goodsID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sys-2018-254118088</td>\n",
       "      <td>user-157213</td>\n",
       "      <td>PR000064</td>\n",
       "      <td>272.51</td>\n",
       "      <td>272.51</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>APP</td>\n",
       "      <td>2018-02-14 12:20:36</td>\n",
       "      <td>2019-02-28 13:38:41</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sys-2018-263312190</td>\n",
       "      <td>user-191121</td>\n",
       "      <td>PR000583</td>\n",
       "      <td>337.93</td>\n",
       "      <td>337.93</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2018-08-14 09:40:34</td>\n",
       "      <td>2019-01-01 14:47:14</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sys-2018-188208169</td>\n",
       "      <td>user-211918</td>\n",
       "      <td>PR000082</td>\n",
       "      <td>905.68</td>\n",
       "      <td>891.23</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>We c hatMP</td>\n",
       "      <td>2018-11-02 20:17:25</td>\n",
       "      <td>2019-01-19 20:06:35</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sys-2018-203314910</td>\n",
       "      <td>user-201322</td>\n",
       "      <td>PR000302</td>\n",
       "      <td>786.27</td>\n",
       "      <td>688.88</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>WEB</td>\n",
       "      <td>2018-11-19 10:36:39</td>\n",
       "      <td>2019-08-07 12:24:35</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sys-2018-283989279</td>\n",
       "      <td>user-120872</td>\n",
       "      <td>PR000290</td>\n",
       "      <td>550.77</td>\n",
       "      <td>542.51</td>\n",
       "      <td>渠道-9527</td>\n",
       "      <td>APP</td>\n",
       "      <td>2018-12-26 11:19:16</td>\n",
       "      <td>2019-10-01 07:42:43</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               orderID       userID   goodsID  orderAmount  payment chanelID  \\\n",
       "id                                                                             \n",
       "1   sys-2018-254118088  user-157213  PR000064       272.51   272.51  渠道-0396   \n",
       "2   sys-2018-263312190  user-191121  PR000583       337.93   337.93  渠道-0765   \n",
       "3   sys-2018-188208169  user-211918  PR000082       905.68   891.23  渠道-0530   \n",
       "4   sys-2018-203314910  user-201322  PR000302       786.27   688.88  渠道-0530   \n",
       "5   sys-2018-283989279  user-120872  PR000290       550.77   542.51  渠道-9527   \n",
       "\n",
       "   platfromType           orderTime             payTime chargeback  \n",
       "id                                                                  \n",
       "1          APP  2018-02-14 12:20:36 2019-02-28 13:38:41          否  \n",
       "2     Wech atMP 2018-08-14 09:40:34 2019-01-01 14:47:14          是  \n",
       "3    We c hatMP 2018-11-02 20:17:25 2019-01-19 20:06:35          否  \n",
       "4          WEB  2018-11-19 10:36:39 2019-08-07 12:24:35          否  \n",
       "5          APP  2018-12-26 11:19:16 2019-10-01 07:42:43          否  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df = pd.read_excel('某电商网站订单数据.xlsx', index_col='id')\n",
    "order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dbbaf962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 104557 entries, 1 to 104557\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   orderID       104557 non-null  object        \n",
      " 1   userID        104557 non-null  object        \n",
      " 2   goodsID       104557 non-null  object        \n",
      " 3   orderAmount   104557 non-null  float64       \n",
      " 4   payment       104557 non-null  float64       \n",
      " 5   chanelID      104549 non-null  object        \n",
      " 6   platfromType  104557 non-null  object        \n",
      " 7   orderTime     104557 non-null  datetime64[ns]\n",
      " 8   payTime       104557 non-null  datetime64[ns]\n",
      " 9   chargeback    104557 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), object(6)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "order_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "74cb4dc2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_df.drop(columns='goodsID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f9c28b46",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 去除重复值\n",
    "order_df.drop_duplicates('orderID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bcae8b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104530, 9)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除之后的形状\n",
    "order_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbbb343b",
   "metadata": {},
   "source": [
    "##### 提取2019年的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "edcbf200",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from datetime import datetime\n",
    "start = datetime(2019, 1, 1)\n",
    "end = datetime(2019, 12, 31, 23, 59, 59)\n",
    "order_df.drop(order_df[order_df.orderTime < start].index, inplace=True)\n",
    "order_df.drop(order_df[order_df.orderTime > end].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b3d4576f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104297</th>\n",
       "      <td>sys-2019-344079195</td>\n",
       "      <td>user-182248</td>\n",
       "      <td>831.29</td>\n",
       "      <td>766.07</td>\n",
       "      <td>渠道-0896</td>\n",
       "      <td>We c hatMP</td>\n",
       "      <td>2019-12-31 23:32:55</td>\n",
       "      <td>2019-12-31 23:33:06</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104298</th>\n",
       "      <td>sys-2019-296195955</td>\n",
       "      <td>user-143322</td>\n",
       "      <td>1565.67</td>\n",
       "      <td>1414.89</td>\n",
       "      <td>渠道-0007</td>\n",
       "      <td>APP</td>\n",
       "      <td>2019-12-31 23:33:05</td>\n",
       "      <td>2019-12-31 23:34:36</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104299</th>\n",
       "      <td>sys-2019-382387725</td>\n",
       "      <td>user-220484</td>\n",
       "      <td>3326.83</td>\n",
       "      <td>3273.54</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>WE B</td>\n",
       "      <td>2019-12-31 23:37:30</td>\n",
       "      <td>2019-12-31 23:37:44</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104300</th>\n",
       "      <td>sys-2019-303891464</td>\n",
       "      <td>user-285133</td>\n",
       "      <td>241.75</td>\n",
       "      <td>241.75</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2019-12-31 23:38:43</td>\n",
       "      <td>2019-12-31 23:39:01</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104301</th>\n",
       "      <td>sys-2019-291405901</td>\n",
       "      <td>user-298747</td>\n",
       "      <td>442.85</td>\n",
       "      <td>339.78</td>\n",
       "      <td>渠道-0283</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2019-12-31 23:48:34</td>\n",
       "      <td>2019-12-31 23:49:04</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   orderID       userID  orderAmount  payment chanelID  \\\n",
       "id                                                                       \n",
       "104297  sys-2019-344079195  user-182248       831.29   766.07  渠道-0896   \n",
       "104298  sys-2019-296195955  user-143322      1565.67  1414.89  渠道-0007   \n",
       "104299  sys-2019-382387725  user-220484      3326.83  3273.54  渠道-0530   \n",
       "104300  sys-2019-303891464  user-285133       241.75   241.75  渠道-0765   \n",
       "104301  sys-2019-291405901  user-298747       442.85   339.78  渠道-0283   \n",
       "\n",
       "       platfromType           orderTime             payTime chargeback  \n",
       "id                                                                      \n",
       "104297   We c hatMP 2019-12-31 23:32:55 2019-12-31 23:33:06          否  \n",
       "104298         APP  2019-12-31 23:33:05 2019-12-31 23:34:36          是  \n",
       "104299        WE B  2019-12-31 23:37:30 2019-12-31 23:37:44          否  \n",
       "104300    Wech atMP 2019-12-31 23:38:43 2019-12-31 23:39:01          是  \n",
       "104301    Wech atMP 2019-12-31 23:48:34 2019-12-31 23:49:04          否  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 验证是否是2019年数据\n",
    "order_df.head()\n",
    "order_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98939c0d",
   "metadata": {},
   "source": [
    "##### 错误数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "631052f1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# 1.支付时间早于下单时间\n",
    "order_df.drop(order_df[order_df.payTime < order_df.orderTime].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1cc61e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [orderID, userID, orderAmount, payment, chanelID, platfromType, orderTime, payTime, chargeback]\n",
       "Index: []"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df[order_df.payTime < order_df.orderTime] # 验证一下时候删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "98deb002",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 2.处理支付时常超过30分钟的数据\n",
    "order_df.drop(order_df[(order_df.payTime - order_df.orderTime).dt.days > 0].index, inplace=True)\n",
    "order_df.drop(order_df[(order_df.payTime - order_df.orderTime).dt.seconds > 1800].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4874b23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [orderID, userID, orderAmount, payment, chanelID, platfromType, orderTime, payTime, chargeback]\n",
       "Index: []"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # 检验是否删除成功\n",
    "order_df[(order_df.payTime - order_df.orderTime).dt.days > 0]\n",
    "order_df[(order_df.payTime - order_df.orderTime).dt.seconds > 1800]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e9cbafe6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3. 处理订单金额或者支付金额小于0的\n",
    "order_df.drop(order_df[(order_df['payment'] < 0)].index, inplace=True)\n",
    "# order_df[(order_df['orderAmount'] < 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5a1ae11c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [orderID, userID, orderAmount, payment, chanelID, platfromType, orderTime, payTime, chargeback]\n",
       "Index: []"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查是否删除干净\n",
    "order_df[order_df.payment < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613bc451",
   "metadata": {},
   "source": [
    "##### 处理渠道为空的字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "489808ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11598</th>\n",
       "      <td>sys-2019-353765060</td>\n",
       "      <td>user-120690</td>\n",
       "      <td>534.12</td>\n",
       "      <td>477.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WEB</td>\n",
       "      <td>2019-03-02 10:11:38</td>\n",
       "      <td>2019-03-02 10:11:55</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11639</th>\n",
       "      <td>sys-2019-339868263</td>\n",
       "      <td>user-264491</td>\n",
       "      <td>206.33</td>\n",
       "      <td>206.330000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>We c hatMP</td>\n",
       "      <td>2019-03-02 14:02:58</td>\n",
       "      <td>2019-03-02 14:03:22</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14234</th>\n",
       "      <td>sys-2019-313502796</td>\n",
       "      <td>user-180054</td>\n",
       "      <td>669.09</td>\n",
       "      <td>669.090000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>We c hatMP</td>\n",
       "      <td>2019-03-16 15:13:31</td>\n",
       "      <td>2019-03-16 15:13:55</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35716</th>\n",
       "      <td>sys-2019-300339928</td>\n",
       "      <td>user-270141</td>\n",
       "      <td>328.83</td>\n",
       "      <td>295.470000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2019-06-06 11:03:46</td>\n",
       "      <td>2019-06-06 11:04:19</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55444</th>\n",
       "      <td>sys-2019-286784634</td>\n",
       "      <td>user-183770</td>\n",
       "      <td>488.07</td>\n",
       "      <td>476.810000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AP P</td>\n",
       "      <td>2019-08-04 18:53:34</td>\n",
       "      <td>2019-08-04 18:53:49</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62378</th>\n",
       "      <td>sys-2019-288609013</td>\n",
       "      <td>user-213725</td>\n",
       "      <td>1316.69</td>\n",
       "      <td>10496.526809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2019-08-26 23:55:30</td>\n",
       "      <td>2019-08-26 23:56:57</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77890</th>\n",
       "      <td>sys-2019-251942165</td>\n",
       "      <td>user-100835</td>\n",
       "      <td>3613.63</td>\n",
       "      <td>3545.980000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2019-10-15 22:59:12</td>\n",
       "      <td>2019-10-15 22:59:28</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86627</th>\n",
       "      <td>sys-2019-322891956</td>\n",
       "      <td>user-116711</td>\n",
       "      <td>802.18</td>\n",
       "      <td>748.850000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2019-11-13 00:06:21</td>\n",
       "      <td>2019-11-13 00:06:39</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  orderID       userID  orderAmount       payment chanelID  \\\n",
       "id                                                                           \n",
       "11598  sys-2019-353765060  user-120690       534.12    477.100000      NaN   \n",
       "11639  sys-2019-339868263  user-264491       206.33    206.330000      NaN   \n",
       "14234  sys-2019-313502796  user-180054       669.09    669.090000      NaN   \n",
       "35716  sys-2019-300339928  user-270141       328.83    295.470000      NaN   \n",
       "55444  sys-2019-286784634  user-183770       488.07    476.810000      NaN   \n",
       "62378  sys-2019-288609013  user-213725      1316.69  10496.526809      NaN   \n",
       "77890  sys-2019-251942165  user-100835      3613.63   3545.980000      NaN   \n",
       "86627  sys-2019-322891956  user-116711       802.18    748.850000      NaN   \n",
       "\n",
       "      platfromType           orderTime             payTime chargeback  \n",
       "id                                                                     \n",
       "11598         WEB  2019-03-02 10:11:38 2019-03-02 10:11:55          否  \n",
       "11639   We c hatMP 2019-03-02 14:02:58 2019-03-02 14:03:22          否  \n",
       "14234   We c hatMP 2019-03-16 15:13:31 2019-03-16 15:13:55          否  \n",
       "35716    Wech atMP 2019-06-06 11:03:46 2019-06-06 11:04:19          否  \n",
       "55444        AP P  2019-08-04 18:53:34 2019-08-04 18:53:49          否  \n",
       "62378    Wech atMP 2019-08-26 23:55:30 2019-08-26 23:56:57          否  \n",
       "77890    Wech atMP 2019-10-15 22:59:12 2019-10-15 22:59:28          否  \n",
       "86627    Wech atMP 2019-11-13 00:06:21 2019-11-13 00:06:39          否  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看渠道为空的数据\n",
    "order_df[order_df.chanelID.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "7a725a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_df.rename(columns={'chanelID': 'channelID'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "7c98df80",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_channelID = order_df.channelID.mode()[0]\n",
    "order_df.channelID.fillna(mode_channelID, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "98a3b729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 103321 entries, 6 to 104301\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   orderID       103321 non-null  object        \n",
      " 1   userID        103321 non-null  object        \n",
      " 2   orderAmount   103321 non-null  float64       \n",
      " 3   payment       103321 non-null  float64       \n",
      " 4   channelID     103321 non-null  object        \n",
      " 5   platfromType  103321 non-null  object        \n",
      " 6   orderTime     103321 non-null  datetime64[ns]\n",
      " 7   payTime       103321 non-null  datetime64[ns]\n",
      " 8   chargeback    103321 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), object(5)\n",
      "memory usage: 11.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# 检验是否填充完毕\n",
    "order_df[order_df.channelID.isna()]\n",
    "order_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81eb02a",
   "metadata": {},
   "source": [
    "##### 处理平台类型（去掉多余空格，保持数据一致）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "6a61d6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_df.rename(columns={'platfromType': 'platformType'}, inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3a82402e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统一写法，将空格去掉，将字母全部小写， 这里是正则匹配的写法\n",
    "order_df.platformType = order_df.platformType.str.replace(r'\\s', '', regex=True).str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "5b61eb1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>channelID</th>\n",
       "      <th>platformType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sys-2019-279103297</td>\n",
       "      <td>user-146548</td>\n",
       "      <td>425.20</td>\n",
       "      <td>425.20</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 00:12:23</td>\n",
       "      <td>2019-01-01 00:13:37</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sys-2019-316686066</td>\n",
       "      <td>user-104210</td>\n",
       "      <td>1764.37</td>\n",
       "      <td>1707.04</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 00:23:06</td>\n",
       "      <td>2019-01-01 00:23:32</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sys-2019-306447069</td>\n",
       "      <td>user-104863</td>\n",
       "      <td>499.41</td>\n",
       "      <td>480.42</td>\n",
       "      <td>渠道-0007</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 01:05:50</td>\n",
       "      <td>2019-01-01 01:06:17</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sys-2019-290267674</td>\n",
       "      <td>user-206155</td>\n",
       "      <td>1103.00</td>\n",
       "      <td>1050.95</td>\n",
       "      <td>渠道-0330</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 01:16:12</td>\n",
       "      <td>2019-01-01 01:16:25</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sys-2019-337079027</td>\n",
       "      <td>user-137939</td>\n",
       "      <td>465.41</td>\n",
       "      <td>465.41</td>\n",
       "      <td>渠道-9527</td>\n",
       "      <td>alimp</td>\n",
       "      <td>2019-01-01 01:31:00</td>\n",
       "      <td>2019-01-01 01:31:36</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sys-2019-417411381</td>\n",
       "      <td>user-181957</td>\n",
       "      <td>279.53</td>\n",
       "      <td>279.53</td>\n",
       "      <td>渠道-0007</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 01:36:17</td>\n",
       "      <td>2019-01-01 01:36:56</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sys-2019-254286596</td>\n",
       "      <td>user-174586</td>\n",
       "      <td>622.70</td>\n",
       "      <td>622.70</td>\n",
       "      <td>渠道-0283</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 01:37:00</td>\n",
       "      <td>2019-01-01 01:37:14</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sys-2019-303647260</td>\n",
       "      <td>user-178023</td>\n",
       "      <td>969.61</td>\n",
       "      <td>913.58</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 02:11:23</td>\n",
       "      <td>2019-01-01 02:12:56</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sys-2019-347419495</td>\n",
       "      <td>user-209896</td>\n",
       "      <td>279.18</td>\n",
       "      <td>225.15</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 02:31:13</td>\n",
       "      <td>2019-01-01 02:32:40</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sys-2019-384544993</td>\n",
       "      <td>user-148994</td>\n",
       "      <td>3424.78</td>\n",
       "      <td>3424.78</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 02:57:16</td>\n",
       "      <td>2019-01-01 02:57:42</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               orderID       userID  orderAmount  payment channelID  \\\n",
       "id                                                                    \n",
       "6   sys-2019-279103297  user-146548       425.20   425.20   渠道-0765   \n",
       "7   sys-2019-316686066  user-104210      1764.37  1707.04   渠道-0396   \n",
       "8   sys-2019-306447069  user-104863       499.41   480.42   渠道-0007   \n",
       "9   sys-2019-290267674  user-206155      1103.00  1050.95   渠道-0330   \n",
       "10  sys-2019-337079027  user-137939       465.41   465.41   渠道-9527   \n",
       "11  sys-2019-417411381  user-181957       279.53   279.53   渠道-0007   \n",
       "12  sys-2019-254286596  user-174586       622.70   622.70   渠道-0283   \n",
       "13  sys-2019-303647260  user-178023       969.61   913.58   渠道-0765   \n",
       "14  sys-2019-347419495  user-209896       279.18   225.15   渠道-0396   \n",
       "15  sys-2019-384544993  user-148994      3424.78  3424.78   渠道-0530   \n",
       "\n",
       "   platformType           orderTime             payTime chargeback  \n",
       "id                                                                  \n",
       "6      wechatmp 2019-01-01 00:12:23 2019-01-01 00:13:37          否  \n",
       "7      wechatmp 2019-01-01 00:23:06 2019-01-01 00:23:32          否  \n",
       "8      wechatmp 2019-01-01 01:05:50 2019-01-01 01:06:17          否  \n",
       "9           app 2019-01-01 01:16:12 2019-01-01 01:16:25          否  \n",
       "10        alimp 2019-01-01 01:31:00 2019-01-01 01:31:36          否  \n",
       "11          app 2019-01-01 01:36:17 2019-01-01 01:36:56          否  \n",
       "12     wechatmp 2019-01-01 01:37:00 2019-01-01 01:37:14          否  \n",
       "13          app 2019-01-01 02:11:23 2019-01-01 02:12:56          否  \n",
       "14          app 2019-01-01 02:31:13 2019-01-01 02:32:40          否  \n",
       "15     wechatmp 2019-01-01 02:57:16 2019-01-01 02:57:42          否  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da083057",
   "metadata": {},
   "source": [
    "##### 5. 添加折扣字段，处理折扣大于1的字段（将支付金额改为‘订单金额’ * ‘平均折扣’）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "8b41ebac",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_df['discount'] = np.round(order_df.payment / order_df.orderAmount, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "f6578eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意计算平均折扣一定要在正常范围内计算, 找出所有折扣小于等于1的数据取折扣一列数值\n",
    "mean_discount = np.mean(order_df[order_df.discount <= 1].discount)\n",
    "\n",
    "# 先将折扣改成正确的 \n",
    "order_df.discount = order_df.discount.apply(lambda x: x if x <= 1 else mean_discount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "bf731890",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 再将支付金额改成对的\n",
    "order_df.payment = order_df.orderAmount * order_df.discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "5f8b86c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103321, 10)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75599cc0",
   "metadata": {},
   "source": [
    "#### 数据分析\n",
    "6. 交易总金额（GMV）、总销售额、实际销售额、退货率、客单价\n",
    "7. 每月GMV及趋势分析（折线图）\n",
    "8. 流量渠道来源分析（饼图）\n",
    "9. 周一到周日哪天的下单量最高、每天哪个时段下单量最高（柱状图）\n",
    "10. 用户复购率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fedcb3",
   "metadata": {},
   "source": [
    "##### 计算金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1a699d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gmv': 108495929.7,\n",
       " 'total_payment': 102600896.62845252,\n",
       " 'real_payment': 88915993.09978104,\n",
       " 'chargeback_rate': 0.13173507805770365,\n",
       " 'user_count': 78634,\n",
       " 'arppu': 1130.75759976322}"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算交易总金额（GMV）、总销售额、实际销售额、退货率、客单价(ARPPU)\n",
    "gmv = order_df.orderAmount.sum() # 交易总额 -> 订单总额\n",
    "total_payment = order_df.payment.sum() # 总销售额 -> 用户付的钱\n",
    "real_payment = order_df[order_df.chargeback=='否'].payment.sum() # 实际销售额 -> 除去退货的\n",
    "chargeback_rate = order_df[order_df.chargeback=='是'].payment.count() / order_df.shape[0] # 退货率 -> 退货数量除以总数量\n",
    "user_count = order_df.userID.nunique() # 不重复的用户数量，因为可能一个用户多次购买\n",
    "arppu = real_payment / user_count # 客单价 = 实际销售额 / 用户数\n",
    "\n",
    "l1 = [gmv, total_payment, real_payment, chargeback_rate, user_count, arppu]\n",
    "l2 = 'gmv, total_payment, real_payment, chargeback_rate, user_count, arppu'.split(', ')\n",
    "mp = dict(zip(l2, l1))\n",
    "mp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73fb4c92",
   "metadata": {},
   "source": [
    "##### 月维度GMV趋势分析（趋势折线图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "7e34149e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 每个月的GMV(订单总和)\n",
    "# 加一列月份\n",
    "order_df['month'] = order_df.orderTime.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "5453472e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['orderID', 'userID', 'orderAmount', 'payment', 'channelID',\n",
       "       'platformType', 'orderTime', 'payTime', 'chargeback', 'discount',\n",
       "       'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看是否加上了\n",
    "order_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "4909c835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先按月分组，然后找到orderAmount，统计求和，这样就得到了每月的gmv（交易总额）\n",
    "# 每个都除以10000然后以万元为单位\n",
    "gmv_by_month = order_df.groupby('month').orderAmount.sum() / 10000 # 每月交易额\n",
    "\n",
    "# 相似的求出其他\n",
    "pay_by_month = order_df.groupby('month').payment.sum() / 10000 # 每月销售额\n",
    "real_by_month = order_df[order_df.chargeback=='否'].groupby('month').payment.sum() / 10000# 每月实际销售额\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "0ac76f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化过程，折线图展示\n",
    "plt.figure(figsize=(6, 4), dpi=120) # 加dpi是为了让图像显示更加清楚\n",
    "gmv_by_month.plot(kind='line', label='每月交易额')\n",
    "pay_by_month.plot(kind='line', label='每月销售额')\n",
    "real_by_month.plot(kind='line', label='每月实际销售额', marker='*')\n",
    "plt.title('每月交易额和销售额')\n",
    "plt.legend() # 图例\n",
    "plt.xticks(list(range(1, 13))) # x轴刻度\n",
    "plt.xlabel('月份') # x轴标注\n",
    "plt.ylabel('金额(万元)') # y轴标注\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5) # 网格线\n",
    "for i in range(1, 13):\n",
    "    plt.text(i, real_by_month[i]-20, np.round(real_by_month[i], 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a64f56f",
   "metadata": {},
   "source": [
    "##### 流量渠道来源分析（占比饼图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "81531e2b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channelID\n",
       "渠道-0007     8613932.61\n",
       "渠道-0168     4371562.74\n",
       "渠道-0191     3378255.19\n",
       "渠道-0283     6559076.14\n",
       "渠道-0318     3170493.98\n",
       "渠道-0330     2182137.84\n",
       "渠道-0396     8731850.37\n",
       "渠道-0465     4357073.04\n",
       "渠道-0530    12753905.82\n",
       "渠道-0568     4508418.30\n",
       "渠道-0765     9601795.50\n",
       "渠道-0789     3282428.54\n",
       "渠道-0896    17307338.46\n",
       "渠道-0985     6564057.86\n",
       "渠道-9527    13113603.31\n",
       "Name: orderAmount, dtype: float64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 流量渠道来源分析（饼图）\n",
    "gmv_by_channel = order_df.groupby('channelID').orderAmount.sum()\n",
    "gmv_by_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "573b49f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6), dpi= 120)\n",
    "gmv_by_channel.nlargest(10).plot(\n",
    "    kind='pie',\n",
    "    autopct='%.2f%%',\n",
    "    pctdistance=0.8, # 百分比的位置\n",
    "    wedgeprops= { #环状图\n",
    "        'width': 0.35,# 掏空的大小\n",
    "        'linewidth': 1,\n",
    "        'edgecolor': 'white'\n",
    "    }\n",
    ")\n",
    "plt.ylabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d368db",
   "metadata": {},
   "source": [
    "##### 周一到周日哪天下单量最高(对比饼图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "126acc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 想要统计周几下单最高，需要先加一列星期的列\n",
    "# 然后根据星期分组，统计一周内的下单量\n",
    "order_df['weekday'] = order_df.orderTime.dt.weekday\n",
    "ser = order_df.groupby('weekday').orderID.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a1f9c73",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-d76f8df342ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m120\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m# ser.reindex([1, 2, 3, 4, 5, 6, 0])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m ser.plot(\n\u001b[0;32m      4\u001b[0m     \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=120)\n",
    "# ser.reindex([1, 2, 3, 4, 5, 6, 0])\n",
    "ser.plot(\n",
    "    kind='bar',\n",
    "    color=np.random.rand(7, 3),\n",
    ")\n",
    "plt.xticks(ser.index, labels=[f'周{i}' for i in '日一二三四五六'], rotation=0) # 找到位置，标记就行了\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "210ba4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 每天那个时段下单量最高\n",
    "# 想要统计哪个时段的下单量最高，先要分时段，这里需要做取整操作，将时段分的清楚\n",
    "# 处理完成之后取time，就只拿到了时间,然后以时间分组\n",
    "order_df['time'] = order_df.orderTime.dt.floor('30T').dt.time\n",
    "order_df.head(10)\n",
    "ser1 = order_df.groupby('time').orderID.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "e05fe3e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=120)\n",
    "ser1.plot(\n",
    "    kind='bar',\n",
    "    color=np.random.rand(48, 3)\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "880067d3",
   "metadata": {},
   "source": [
    "##### 计算用户复购率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "22890af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.pivot_table(data=order_df, index=['userID'], columns='month', values=['orderID'], aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0afd6085",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算复购率小技巧，将所有的1变成0，将所有的大于1的变成1，这样计算复购的时候可以求和\n",
    "# 计算购买的人数的时候可以使用count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "c12e540f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 78634 entries, user-100000 to user-299995\n",
      "Data columns (total 12 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   (orderID, 1)   6241 non-null   float64\n",
      " 1   (orderID, 2)   4907 non-null   float64\n",
      " 2   (orderID, 3)   5796 non-null   float64\n",
      " 3   (orderID, 4)   6483 non-null   float64\n",
      " 4   (orderID, 5)   9360 non-null   float64\n",
      " 5   (orderID, 6)   10347 non-null  float64\n",
      " 6   (orderID, 7)   9356 non-null   float64\n",
      " 7   (orderID, 8)   9259 non-null   float64\n",
      " 8   (orderID, 9)   9141 non-null   float64\n",
      " 9   (orderID, 10)  8704 non-null   float64\n",
      " 10  (orderID, 11)  10680 non-null  float64\n",
      " 11  (orderID, 12)  10365 non-null  float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 7.8+ MB\n"
     ]
    }
   ],
   "source": [
    "def handle_data(x):\n",
    "    return 1 if x > 1 else 0 if x == 1 else np.nan\n",
    "temp = temp.applymap(handle_data)\n",
    "temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "d54e0a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         month\n",
       "orderID  1        1.826630\n",
       "         2        1.141227\n",
       "         3        1.742581\n",
       "         4        2.020669\n",
       "         5        3.055556\n",
       "         6        3.054025\n",
       "         7        2.522445\n",
       "         8        3.164489\n",
       "         9        2.472377\n",
       "         10       2.791820\n",
       "         11       2.911985\n",
       "         12       3.087313\n",
       "dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 复购率\n",
    "repurchase_rate = temp.sum() / temp.count() * 100\n",
    "repurchase_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7bb802",
   "metadata": {},
   "source": [
    "#### 数据建模\n",
    "- RFM 模型\n",
    "    - R Recency - 最近一次购买 --->低\n",
    "    - F Frequency - 购买的频次 --->高\n",
    "    - M Monetary - 花费的金额  --->高\n",
    "![](rfm-model.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "fea103e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除退了货的用户\n",
    "temp_df = order_df.drop(order_df[order_df.chargeback == '是'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "3bb99b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((89710, 13), (103321, 13))"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.shape, order_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "7547b1a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>orderTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>1</td>\n",
       "      <td>1978.47</td>\n",
       "      <td>2019-10-13 18:46:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>521.60</td>\n",
       "      <td>2019-05-24 13:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>1</td>\n",
       "      <td>466.89</td>\n",
       "      <td>2019-11-14 15:37:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>2178.20</td>\n",
       "      <td>2019-01-14 18:45:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>1</td>\n",
       "      <td>4949.65</td>\n",
       "      <td>2019-11-16 17:15:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>1</td>\n",
       "      <td>441.71</td>\n",
       "      <td>2019-10-18 10:53:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>1</td>\n",
       "      <td>706.80</td>\n",
       "      <td>2019-12-27 17:57:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>2</td>\n",
       "      <td>1685.18</td>\n",
       "      <td>2019-11-11 10:40:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>508.75</td>\n",
       "      <td>2019-01-01 16:14:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>479.94</td>\n",
       "      <td>2019-03-30 16:35:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             F  orderAmount           orderTime\n",
       "userID                                         \n",
       "user-100000  1      1978.47 2019-10-13 18:46:46\n",
       "user-100003  1       521.60 2019-05-24 13:04:05\n",
       "user-100006  1       466.89 2019-11-14 15:37:19\n",
       "user-100007  1      2178.20 2019-01-14 18:45:35\n",
       "user-100008  1      4949.65 2019-11-16 17:15:03\n",
       "...         ..          ...                 ...\n",
       "user-299980  1       441.71 2019-10-18 10:53:37\n",
       "user-299983  1       706.80 2019-12-27 17:57:11\n",
       "user-299989  2      1685.18 2019-11-11 10:40:08\n",
       "user-299992  1       508.75 2019-01-01 16:14:47\n",
       "user-299995  1       479.94 2019-03-30 16:35:12\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 接下来建立RFM模型\n",
    "# 流程就是分别添加RFM列，然后根据RMF值，确定用户类型\n",
    "# 首先添加F列\n",
    "temp_df['F'] = 1\n",
    "res_df = temp_df.pivot_table(index='userID', values=['orderTime', 'F', 'orderAmount'],\n",
    "                    aggfunc={\n",
    "                        'orderTime': max,\n",
    "                        'F': sum,\n",
    "                        'orderAmount': sum\n",
    "                    }) # 使用字典代表每个列使用的函数\n",
    "res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69e1593",
   "metadata": {},
   "source": [
    "##### 添加RFM列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "739b5d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>1</td>\n",
       "      <td>1978.47</td>\n",
       "      <td>2019-10-13 18:46:46</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>521.60</td>\n",
       "      <td>2019-05-24 13:04:05</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>1</td>\n",
       "      <td>466.89</td>\n",
       "      <td>2019-11-14 15:37:19</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>2178.20</td>\n",
       "      <td>2019-01-14 18:45:35</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>1</td>\n",
       "      <td>4949.65</td>\n",
       "      <td>2019-11-16 17:15:03</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>1</td>\n",
       "      <td>441.71</td>\n",
       "      <td>2019-10-18 10:53:37</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>1</td>\n",
       "      <td>706.80</td>\n",
       "      <td>2019-12-27 17:57:11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>2</td>\n",
       "      <td>1685.18</td>\n",
       "      <td>2019-11-11 10:40:08</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>508.75</td>\n",
       "      <td>2019-01-01 16:14:47</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>479.94</td>\n",
       "      <td>2019-03-30 16:35:12</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             F  orderAmount           orderTime    R\n",
       "userID                                              \n",
       "user-100000  1      1978.47 2019-10-13 18:46:46   79\n",
       "user-100003  1       521.60 2019-05-24 13:04:05  221\n",
       "user-100006  1       466.89 2019-11-14 15:37:19   47\n",
       "user-100007  1      2178.20 2019-01-14 18:45:35  351\n",
       "user-100008  1      4949.65 2019-11-16 17:15:03   45\n",
       "...         ..          ...                 ...  ...\n",
       "user-299980  1       441.71 2019-10-18 10:53:37   74\n",
       "user-299983  1       706.80 2019-12-27 17:57:11    4\n",
       "user-299989  2      1685.18 2019-11-11 10:40:08   50\n",
       "user-299992  1       508.75 2019-01-01 16:14:47  364\n",
       "user-299995  1       479.94 2019-03-30 16:35:12  276\n",
       "\n",
       "[70592 rows x 4 columns]"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加R列，统计最近一次购买的相距离本年度最后一天的天数\n",
    "cur_date = datetime(2019, 12, 31, 23, 59, 59)\n",
    "res_df['R'] =  (cur_date - res_df['orderTime']).dt.days\n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "4e97e4b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 添加M列\n",
    "res_df['M'] = res_df.orderAmount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "6de6ea5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除其他列\n",
    "res_df.drop(['orderAmount', 'orderTime'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "337a3bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看分布，决定使用什么值来作为大小之间的标准\n",
    "import seaborn as sns\n",
    "# sns.distplot(res_df.orderAmount)\n",
    "# sns.distplot(res_df.R)\n",
    "sns.displot(res_df.F)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "908de71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df = res_df.reindex(columns=['R', 'F', 'M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "947638c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 与中位数作差然后将大于中位数的标记为1， 小于的标记为0\n",
    "temp_df = res_df.apply(lambda x: x-x.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e03f326",
   "metadata": {},
   "source": [
    "##### 添加标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "b8bea8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 得到rmf模型\n",
    "rfm_model = temp_df.applymap(lambda x: '1' if x >= 0 else '0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "767a01e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义映射函数\n",
    "tags_dict = {\n",
    "        '111': '重要价值用户',\n",
    "        '101': '重要发展用户',\n",
    "        '011': '重要保持用户',\n",
    "        '001': '重要挽留用户',\n",
    "        '110': '一般价值用户',\n",
    "        '100': '一般发展用户',\n",
    "        '010': '一般保持用户',\n",
    "        '000': '一般挽留用户',\n",
    "    }\n",
    "def make_tag(model):\n",
    "    key = model['R'] + model['F'] + model['M']\n",
    "    return tags_dict[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "57e641d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_model['label'] = rfm_model.apply(make_tag, axis=1)\n",
    "rfm_model.rename(columns={'label': 'TAG'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "206073b5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TAG\n",
       "一般价值用户     1373\n",
       "一般保持用户     3836\n",
       "一般发展用户    22311\n",
       "一般挽留用户    19813\n",
       "重要价值用户     2684\n",
       "重要保持用户     8062\n",
       "重要发展用户     6901\n",
       "重要挽留用户     5612\n",
       "Name: TAG, dtype: int64"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = rfm_model.groupby('TAG').TAG.count()\n",
    "ser2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d812bc5",
   "metadata": {},
   "source": [
    "##### 查看占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "b6ecf4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6), dpi=120)\n",
    "ser2.plot(\n",
    "    kind='pie', \n",
    "    autopct='%.2f%%',\n",
    "    pctdistance=0.8,\n",
    "    wedgeprops={\n",
    "        'width': 0.35,\n",
    "        'edgecolor':'white',\n",
    "        'linewidth': 1\n",
    "        \n",
    "    }\n",
    "         \n",
    ")\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89eea980",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
